Link Strength Prediction Using Transaction-Based Matrix Factorization

The revolution of social networks and methods of analyzing them have attracted interest in many research fields. Predicting whether a friendship holds in a social network between two individuals or not, link prediction, has been a heavily researched topic in the last decade. In this research I've investigated a related problem, link strength prediction: how to assign strengths to friendship links. A basic approach would be matrix factorization applied to only friendship ratings. However, the existence of transactions among users may be used for better predictions. I propose a new multiple-matrix factorization model for incorporating a transaction matrix. Multiple-matrix factorization can be seen as a data fusion technique that combines evidence from different sources. In the social network application, the target matrix contains friendship ratings and the evidence matrices specify transaction intensities between users. To evaluate the model, I introduce data from Cloob.com as well as synthetic data.